Science of Machine Learning (was Machine Learning , some thoughts)

Birger Haarbrandt birger.haarbrandt at
Tue Jul 3 06:40:09 EDT 2018

Hi Philippe,

I completely agree with your view. This is why data stewardship is 
needed before we can make real use of the data:

As we use this approach in HiGHmed, I might be able to report in 2020 
about lessons learned :)


*Birger Haarbrandt, M. Sc.
Peter L. Reichertz Institut for Medical Informatics (PLRI)
Technical University Braunschweig and Hannover Medical School
Software Architect HiGHmed Project *
Tel: +49 176 640 94 640, Fax: +49 531/391-9502
birger.haarbrandt at

Am 03.07.2018 um 12:21 schrieb Philippe Ameline:
> Le 02/07/2018 à 11:31, Bert Verhees a écrit :
>> On 30-06-18 17:16, Philippe Ameline wrote:
>>> (improperly labeling images or adding images of objects that are not
>>> plants) could probably make the whole app plainly crappy.
>> Of course Philippe, but that would be vandalism. Most sensible people
>> don't do that when they stand behind the goal, and a little bit of
>> dirt, therefor it is Machine Learning, it can filter it out. It is
>> part of the learning process.
> If a culture of data quality is properly installed, then it is possible
> to name improper use "vandalism".
> In medicine, since such a culture has never existed, we could name it
> "don't carisme", "no time for thisisme" or "was never thaughtisme".
> My point, and what the paper I previously pointed out explains, is that
> trying to get something out of machine learning in a domain of poor data
> quality is a modern kind of magic thinking.
> It just means that any such project should first organize for data
> quality as a first step.
> When considering it in hindsight, it makes sense since machine learning
> involves statistics and data quality is paramount in this domain.
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